Google's AI-powered campaigns – examples of when expert and AI collaboration works best

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Artificial intelligence is bringing unprecedented opportunities to marketing. Google is touting its AI-powered campaigns and tools like Performance Max and smart bidding models that “optimize themselves.” The idea sounds appealing – why would we need experts anymore if algorithms can do it all?

In reality, automation without expert guidance can lead to big mistakes: wasted budget , incorrectly selected target groups , and generic messages that don't resonate with customers.

Artificial intelligence is powerful, but it is by no means a replacement for human expertise and understanding. In this blog, I will examine how the MRACE® model can be used to combine the power of artificial intelligence and expert guidance for effective marketing.





Artificial Intelligence-based Google Tools: Opportunities and Pitfalls from an Expert's Perspective
Google offers a variety of tools that make campaign implementation and optimization more efficient:

Performance Max – Artificial intelligence combines all of Google's platforms (Search, Display, YouTube, Shopping, Gmail, and Maps) into one campaign and automatically optimizes ad targeting.
Smart Bidding models – tROAS aims to optimize the bolivia phone number list campaign so that the return on ad spend reaches the user-set target. Maximize conversions bidding strategy focuses on getting the highest possible number of conversions with a given budget.
Demand Gen – An AI-powered campaign format designed specifically for visual and engagement campaigns. It can easily drive demand. It uses AI to automatically target ads across YouTube, Discover, and Gmail. To be effective, Demand Gen requires close guidance from an expert.
Challenges without an expert:
Targeting too broadly: Campaigns reach a large number of users, but these are not always ready to buy.
Generic messages: Artificial intelligence cannot produce brand-loyal and emotional messages.
Budget overspending: The algorithm focuses on maximizing clicks but does not consider cost-effectiveness.
Artificial intelligence optimizes quantity , but not quality.
Example: The pitfall of the Maximize Conversions strategy
A service company used the Maximize Conversions bidding model to drive as many leads as possible. The campaign met its goals, but closer analysis showed that a large portion of these leads were low-quality inquiries that didn’t convert into sales. AI optimizes quantitative performance, but quality often gets overlooked.

Expert solution:
The expert refined conversion tracking and guided the campaign's target audience, for example by asking the company to add questions to the forms to more effectively screen potential customers. This reduced the number of leads but increased their quality and led to increased sales.

As stated in the Digital Marketing+AI book :

“AI works most effectively when combined with human expertise. Without strategic guidance, AI can only lead to seemingly good results that do not support the overall business strategy.”



Performance Max and the bot traffic challenge
Performance Max campaigns rely on artificial intelligence, but one of their challenges is the inability to identify bot traffic. When a campaign detects high levels of 'activity', such as clicks or impressions, it can optimize ads based on these, even if the traffic is not real customers.

Example:
An online store used Performance Max and noticed that a campaign was receiving large amounts of traffic from a specific geographic location. Further analysis showed that the traffic was mostly bots. The AI ​​didn’t notice the abnormal behavior and increasingly directed budget to this segment.

Expert solution:
The specialist identified abnormal traffic based on analytics and excluded the problematic location from the campaign. This reduced clicks but increased the quality of conversions and improved budget efficiency.

This shows why AI needs expert oversight to ensure that campaign traffic is genuine and delivers real value.